arXiv 2303.032822023-03-06cs.ROcs.SYeess.SY

利用欠激励脉冲发生器阵列的学习对象操纵

Learning Object Manipulation With Under-Actuated Impulse Generator Arrays

Mitsubishi Electric Research Labs（三菱电机研究实验室）

Chuizheng Kong, William Yerazunis, Daniel Nikovski

备注：: Accepted at the 2023 American Control Conference

摘要：

半个多世纪以来，振动碗式喂料机一直是自动化装配的标准，用于小零件的分离、定向和操作。不幸的是，这些馈线是昂贵的，噪音大，高度专业化的一个单一的部分设计的基础。我们考虑一种替代设备和学习控制方法，通过七个固定位置的可变能量螺线管脉冲致动器位于半刚性部分支撑表面下方的分离，定向和操纵。使用计算机视觉来提供零件姿态信息，我们测试了各种机器学习（ML）算法，以生成选择最佳致动器和致动能量的控制策略。我们的操作测试对象是一个6面骰子。使用最合适的ML算法，我们能够用单个脉冲将骰子翻转到任何所需的面30.4%的时间，用两个选择的脉冲将骰子翻转到任何所需的面51.3%，而随机策略成功的时间为5.1%（即，由随机选择的螺线管提供随机选择的脉冲）。

英文摘要：

For more than half a century, vibratory bowl feeders have been the standard in automated assembly for singulation, orientation, and manipulation of small parts. Unfortunately, these feeders are expensive, noisy, and highly specialized on a single part design bases. We consider an alternative device and learning control method for singulation, orientation, and manipulation by means of seven fixed-position variable-energy solenoid impulse actuators located beneath a semi-rigid part supporting surface. Using computer vision to provide part pose information, we tested various machine learning (ML) algorithms to generate a control policy that selects the optimal actuator and actuation energy. Our manipulation test object is a 6-sided craps-style die. Using the most suitable ML algorithm, we were able to flip the die to any desired face 30.4\% of the time with a single impulse, and 51.3\% with two chosen impulses, versus a random policy succeeding 5.1\% of the time (that is, a randomly chosen impulse delivered by a randomly chosen solenoid).

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。

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英文摘要：

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